Automating customer care through self-service solutions (e.g., Interactive Voice Response (IVR), web-based self-care, etc. . . . ) results in substantial cost savings and operational efficiencies. However, due to several factors, such automated systems are unable to provide customers with a quality experience. The present invention addresses some of the deficiencies experienced with presently existing automated care systems.
Machine learning is a field where various algorithms have been developed that can automatically learn from experience. The foundation of these algorithms is built on mathematics and statistics which can be employed to predict events, classify entities, diagnose problems and model function approximations, just to name a few examples. While there are various products available for incorporating machine learning into computerized systems, those products currently suffer from a variety of limitations. For example, they generally lack distributed processing capabilities, and rely heavily on batch and non-transactional data processing. The teachings and techniques of this application can be used to address one or more of the limitations of the prior art to improve the scalability of machine learning solutions.